The effect of genome variation on human proteins: understanding variants and improving their deleteriousness prediction through extensive contextualisation

Rapid technological advances are providing unprecedented insights in the biologicalsciences, with massive amounts of data generated on genomic and protein sequences.These data continue to grow exponentially, and they are extremely valuable for com-putational tools where the effect of genomic variants on human health is predicted.State of the art tools in this field give varying results and only tend to agree in thecase of single variants that are strongly correlated to disease. The aim of this workis to increase the reliability of these methods, as well as our understanding of theunderlying biological mechanisms that lead to disease. We first developed machinelearning (ML) based structural bioinformatics predictors that are able to predictmolecular features of proteins from the sequence alone. We then used these tools forin silico analysis of the molecular effects of known variants on the affected proteins,and integrated these data with other sources heterogenous sources of information,such as the essentiality of a gene, that put the variants into their broader biologicalcontext. With this information we created DEOGEN, a novel predictor in this field,which is able to deal with the two most common forms of genomic variation, namelySingle Nucleotide Variants (SNVs) and short Insertions and DELetions (INDELs).DEOGEN performs at least on par with other state of the art methods in this fieldon different datasets. The method was then extended with additional contextualdata and is now available as DEOGEN2 via a web server, which visualizes the pre-dicted results for all variants in most human proteins through an interactive interfacetargeted to both bioinformaticians and clinicians.Doctorat en Sciences